arXiv:2603.24705v3 Announce Type: replace-cross Abstract: Discrete choice models are fundamental tools in management science, economics, and marketing for understanding and predicting decision-making. Logit-based models are dominant in applied work, largely due to their convenient closed-form expressions for choice probabilities. However, these models entail restrictive assumptions on the stochastic utility component, constraining our ability to capture realistic and theoretically grounded choice behavior$-$most notably, substitution patterns. In this work, we propose an amortized inference ap
The paper leverages recent advancements in equivariant neural network architectures to address long-standing limitations in traditional discrete choice models, which are central to economic and marketing analysis.
Improved discrete choice models can provide a more accurate understanding of complex decision-making, leading to better predictions in market behavior, policy outcomes, and resource allocation.
The application of equivariant neural networks allows for more flexible and realistic modeling of consumer behavior and market dynamics, moving beyond the restrictive assumptions of logit-based approaches.
- · AI/ML researchers
- · Econometricians
- · Marketing analytics firms
- · Consumer goods companies
- · Traditional statistical modeling approaches
- · Firms reliant on simplistic market models
More accurate predictive models for consumer choices in various sectors.
Improved efficiency in resource allocation and policy design due to better understanding of substitution patterns.
Potential for new product development and market segmentation strategies based on granular choice behavior analysis.
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Read at arXiv cs.LG